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Fabrion

ML Ops Engineer — Agentic AI Lab (Founding Team)

11mo ago
USA
Fabrion

ML Ops Engineer — Agentic AI Lab (Founding Team)

11mo ago
USAkubernetesrayterraformdockergithub actionsargocd

Responsible for automating ML model training, deployment, versioning, and observability pipelines in an AI Lab focused on intelligent infrastructure.

Responsibilities

  • Our AI Lab is pioneering the future of intelligent infrastructure through open-source LLMs, agent-native pipelines, retrieval-augmented generation (RAG), and knowledge-graph-grounded models.
  • We’re hiring an ML Ops Engineer to be the glue between ML research and production systems — responsible for automating the model training, deployment, versioning, and observability pipelines that power our agents and AI data fabric.
  • You’ll work across compute orchestration, GPU infrastructure, fine-tuned model lifecycle management, model governance, and security e
  • Build and maintain secure, scalable, and automated pipelines for:
  • LLM fine-tuning, SFT, LoRA, RLHF, DPO training
  • RAG embedding pipelines with dynamic updates
  • Model conversion, quantization, and inference rollout
  • Manage hybrid compute infrastructure (cloud, on-prem, GPU clusters) for training and inference workloads using Kubernetes, Ray, and Terraform
  • Containerize models and agents using Docker, with reproducible builds and CI/CD via GitHub Actions or ArgoCD
  • Implement and enforce model governance: versioning, metadata, lineage, reproducibility, and evaluation capture
  • Create and manage evaluation and benchmarking frameworks (e.g. OpenLLM-Evals, RAGAS, LangSmith)
  • Integrate with security and access control layers (OPA, ABAC, Keycloak) to enforce model policies per tenant
  • Instrument observability for model latency, token usage, performance metrics, error tracing, and drift detection
  • Support deployment of agentic apps with LangGraph, LangChain, and custom inference backends (e.g. vLLM, TGI, Triton)

Nice to have

  • LLM Ops : HuggingFace, DeepSpeed, MLflow, Weights & Biases, DVC
  • Infra : Kubernetes (GKE/EKS), Ray, Terraform, Helm, GitHub Actions, ArgoCD
  • Serving : vLLM, TGI, Triton, Ray Serve
  • Pipelines : Prefect, Airflow, Dagster
  • Monitoring : Prometheus, Grafana, OpenTelemetry, LangSmith
  • Security : OPA (Rego), Keycloak, Vault
  • Languages : Python (primary), Bash, optionally Rust or Go for tooling

Other

  • Location: San Francisco Bay Area
  • Type: Full-Time
  • Compensation: Competitive salary + meaningful equity (founding tier)
  • Backed by 8VC, we're building a world-class team to tackle one of the industry’s most critical infrastructure problems.
  • 4+ years in MLOps, ML platform engineering, or infra-focused ML roles
  • Deep familiarity with model lifecycle management tools: MLflow, Weights & Biases, DVC,
  • HuggingFace Hub
  • Experience with large model deployments (open-source LLMs preferred): LLaMA,
  • Mistral, Falcon, Mixtral
  • Comfortable with tuning libraries (HuggingFace Trainer, DeepSpeed, FSDP, QLoRA)
  • Familiarity with inference serving: vLLM, TGI, Ray Serve, Triton Inference Server
  • Proficient with Terraform, Helm, K8s, and container orchestration
  • Experience with CI/CD for ML (e.g. GitHub Actions + model checkpoints)
  • Managed hybrid workloads across GPU cloud (Lambda, Modal, HuggingFace Inference,
  • Sagemaker)
  • Familiar with cost optimization (spot instance scaling, batch prioritization, model sharding)
  • Agent + Data Pipeline Support: ●
  • Familiarity with LangChain, LangGraph, LlamaIndex or similar RAG/agent orchestration tools
  • Built embedding pipelines for multi-source documents (PDF, JSON, CSV, HTML)
  • Integrated with vector databases (Weaviate, Qdrant, FAISS, Chroma)
  • Implemented model-level RBAC, usage tracking, audit trails
  • Integrated with API rate limits, tenant billing, and SLA observability
  • Experience with policy-as-code systems (OPA, Rego) and access layers
  • Builder's mindset with startup autonomy: you automate what slows you down
  • Obsessive about reproducibility , observability , and traceability
  • Comfortable with a hybrid team of AI researchers, DevOps, and backend engineers
  • Interested in aligning ML systems to product delivery, not just papers
  • Bonus: experience with SOC2, HIPAA, or GovCloud-grade model operations
  • 5+ years as a full stack or backend engineer
  • Experience owning and delivering production systems end-to-end
  • Prior experience with modern frontend frameworks (React, Next.js)
  • Familiarity with building APIs, databases, cloud infrastructure, or deployment workflows at scale
  • Comfortable working in early-stage startups or autonomous roles, prior experience as a founder, founding engineer, or a 0-1 pre-seed startup is a big plus
  • Comfortable with ambiguity, eager to prototype and iterate quickly
  • Strong sense of ownership — prefers to build systems rather than wait for tickets
  • Enjoys thinking about architecture, performance, and tradeoffs at every level
  • Clear communicator and pragmatic team player
  • Values equity and impact over prestige or hierarchy
  • Prior startup or founding team experience
  • Your work will enable models and agents to be trained, evaluated, deployed, and governed at
  • scale — across many tenants, models, and tasks. This is the backbone of a secure, reliable,
  • and scalable AI-native enterprise system. If you dream about using AI to solve some really hard
  • real world problems – we would love to hear from you.
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